{"id":"69297118-bfbf-4047-84df-4cc1053a6485","arxiv_id":"2607.10059","paper_version":1,"verdict":"CONDITIONAL","confidence":"LOW","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Frontier LLM agents achieve at most 59.5% paired accuracy on should-act vs should-abstain tasks, and abstention skill is largely independent of task-solving skill.","lead":"This paper introduces AgentAbstain, a paired-task benchmark that tests whether tool-using LLM agents know when to refuse to act under ambiguity, conflicts, or tool failures. Smart generalists should care because autonomous agents that act when they should not create irreversible real-world risk.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"Abstract-only review cannot verify that controlled perturbations + LLM judges produce valid should-abstain ground truth, which is the load-bearing premise of the 59.5% paired-accuracy claim.","rationale":"The reader correctly flags that the entire empirical claim is conditional on the validity of the paired ground-truth construction, which cannot be audited from the abstract alone. No stronger internal inconsistency or methodological red flag is visible in the abstract text; the design (executable sandboxes, controlled twins, open code, AbstainGen regeneration) is the right shape for the problem. Therefore the CONDITIONAL verdict with LOW confidence is already the appropriate stance; my stress-test does not move it. The concrete test above is the minimal check that would either clear the concern or force a downward revision of the reported numbers. Honest non-finding on any deeper flaw is reported: the load-bearing issue is precisely the one the reader already identified.","tokens_in":2141,"tokens_out":596,"duration_ms":4954,"concrete_test":"Once the full paper or open-sourced dataset is available, draw a stratified sample of 50 paired tasks (covering all 8 scenarios). Have three independent human experts, blinded to model outputs, re-label each twin as should-act / should-abstain / ambiguous. Compute (a) inter-annotator agreement and (b) agreement with the paper’s LLM-judge labels. If human–judge agreement falls below ~90% or >10% of pairs are judged ambiguous/solvable, the headline paired-accuracy numbers and independence claim require re-computation on the cleaned subset.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (best agent 59.5% paired accuracy; abstention largely independent of task-solving) rests entirely on the validity of the should-abstain twin labels. The abstract asserts that each twin is produced by a controlled perturbation of instruction/tool/environment state, then validated by deterministic replay plus semantic LLM judges, with human annotators rating 94–98% of a sample as well-designed. Because the full text is unavailable, none of the following can be checked: (1) whether the 8-scenario taxonomy exhaustively covers the intended risk surface or systematically under-samples hard cases; (2) whether the LLM judges share the same failure modes as the evaluated agents (circular validation); (3) whether “well-designed” ratings equate to correct ground-truth labels rather than surface plausibility; (4) whether paired accuracy is computed only on pairs whose labels survive independent human adjudication. If a non-trivial fraction of should-abstain twins are actually solvable or ambiguous, both the absolute 59.5% figure and the claimed independence from task-solving capability become unreliable. This is exactly the reader’s weakest_assumption, and it remains the single most load-bearing untested condition.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The manuscript introduces AgentAbstain, claimed as the first systematic paired-task benchmark for agentic abstention: whether tool-using LLM agents correctly recognize when not to act. It defines an 8-scenario taxonomy spanning pre-execution reasoning and runtime discovery, with 263 paired tasks (should-act vs. should-abstain twins produced by controlled perturbations of instruction, tool, or environment) across 42 executable sandboxes. AbstainGen is proposed as a fully automated synthesis pipeline validated by deterministic replay and semantic LLM judges, with three annotators rating 94–98% of a sample as well-designed. Evaluating 17 frontier LLMs in 4 agent harnesses, the paper reports a best paired accuracy of 59.5% (Gemini 3.1 Pro) and claims that abstention capability is largely independent of general task-solving capability, with additional failure modes such as post-hoc abstention after irreversible actions.","tokens_in":2404,"tokens_out":1332,"duration_ms":15972,"significance":"If the ground-truth labels and independence analysis hold under scrutiny, this is a timely and practically important contribution: agent evaluations have largely optimized for task success while under-measuring the safety-critical decision of when not to act. The paired design is the right unit of analysis for calibrated abstention; open-sourcing code and data, plus on-demand regeneration against contamination, are genuine strengths. A credible finding that abstention does not track task-solving would correctly redirect the field away from pure capability scaling. Significance therefore hinges almost entirely on label validity and on the statistical support for the independence claim.","major_comments":[{"comment":"Abstract (paired design / AbstainGen validation): The central 59.5% paired-accuracy claim and the independence claim rest on the validity of should-abstain twin labels. Validation is described as deterministic replay plus semantic LLM judges, with human annotators rating 94–98% of a sample as 'well-designed.' 'Well-designed' is not equivalent to correct ground truth (solvable vs. truly unsolvable/ambiguous under the intended policy). Semantic LLM judges used to validate tasks that later evaluate LLMs create a circularity risk if judges share failure modes with the evaluated agents. The manuscript must report (i) independent human adjudication of act/abstain labels (not only design quality), (ii) inter-annotator agreement on the labels themselves, and (iii) the fraction of pairs discarded or flipped under that adjudication. Without this, both headline numbers are unreliable.","section":"Abstract (validation / ground truth)"},{"comment":"Abstract (independence claim): The claim that 'abstention capability is largely independent of general task-solving capability' is load-bearing for the policy conclusion that scaling task-solving alone will not close the gap. The abstract does not state the correlation measure, controls (same harness, same model family, same tool budget), or whether independence holds within vs. across harnesses. This analysis must be reported with effect sizes and confidence intervals; if it is only a qualitative scatter observation, the claim should be weakened.","section":"Abstract (results / independence)"},{"comment":"Abstract (8-scenario taxonomy / 263 pairs): The taxonomy and coverage are asserted rather than justified against a threat model of real agentic harm (irreversible tool use, conflicting constraints, partial observability, etc.). If hard or high-stakes scenarios are systematically under-sampled, the 59.5% figure overstates readiness. The paper needs an explicit mapping from risk surface to the 8 scenarios, plus a coverage or difficulty audit (e.g., human solvability of should-act sides; ambiguity rates on should-abstain sides).","section":"Abstract (taxonomy / benchmark construction)"},{"comment":"Abstract (paired accuracy definition): Paired accuracy requires correctness on both the act and abstain sides. The abstract does not specify how partial credit, tool-call traces, or post-hoc abstention after irreversible actions are scored, nor whether pairs with ambiguous labels are excluded. Scoring rules for 'post-hoc abstention' (named as a failure mode) must be explicit, because counting an irreversible action followed by a verbal abstention as a success would inflate abstention metrics.","section":"Abstract (metrics / failure modes)"}],"minor_comments":[{"comment":"The abstract packs many design claims (263 pairs, 42 sandboxes, 17 models, 4 harnesses, 8 scenarios, 94–98% ratings) without pointing to tables or appendices; once the full text is available, each of these should be traceable to a single table or figure.","section":"Abstract"},{"comment":"Model name 'Gemini 3.1 Pro' should be checked for public naming consistency at publication time to avoid irreproducible model identifiers.","section":"Abstract (results)"},{"comment":"The phrase 'parameter-free' is not used, but 'controlled perturbation' should be defined operationally (what is held fixed vs. varied) so that others can regenerate twins without author-specific judgment.","section":"Abstract (paired design)"},{"comment":"Open-sourcing at agentabstain.github.io is welcome; the camera-ready should pin commit hashes and regeneration seeds so that 'fresh task instances' are actually reproducible.","section":"Abstract (resources)"}],"recommendation":"uncertain","confidential_remarks":"This is an abstract-only review; full text was unavailable. I cannot responsibly recommend accept/minor/major/reject without inspecting perturbation validity, judge prompts, human label adjudication, and the independence analysis. The stress-test concern (circular LLM-judge validation and 'well-designed' ≠ correct ground truth) is the single load-bearing risk and is not resolvable from the abstract. If the full paper provides independent human label adjudication with high agreement and a transparent correlation analysis, this could become a strong empirical contribution; if not, the headline numbers should not be trusted. Scope fit for a serious AI/ML venue is good if the methodology section is rigorous."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"Punchline: this is a paired act/abstain benchmark for tool-using LLM agents, with a claim that the best frontier setup only hits 59.5% paired accuracy and that abstention barely tracks general task skill. If the labels are clean, that is a useful correction to how we evaluate agents.\n\nWhat is actually new is the packaging, not the slogan “agents should know when to stop.” They build an 8-scenario agent-native taxonomy, 263 should-act / should-abstain pairs over 42 executable sandboxes, and AbstainGen to regenerate tasks against contamination. The paired accuracy metric is the right unit: success on the act side alone is not enough. Open code and sandboxes, deterministic replay, and multi-annotator “well-designed” ratings (94–98% on a sample) are real credit. The post-hoc abstention failure mode—act first, refuse later—is the kind of concrete finding people in agent safety will use.\n\nSoft spots, in proportion. We only have the abstract. The load-bearing premise is that controlled perturbations plus LLM judges plus human ratings produce valid should-abstain ground truth. If a non-trivial slice of twins is still solvable or merely ambiguous, both the 59.5% number and the independence claim wobble. Semantic LLM judges validating tasks that later score LLMs is a mild circularity risk, not a disqualifier, but it needs independent human adjudication of labels, not just “well-designed” surface ratings. Taxonomy coverage and harness fairness are also uncheckable here. None of that is a reason to dismiss the work; it is a reason to read the full methods before citing the headline number.\n\nWho it is for: people building or evaluating tool-using agents, especially anyone treating task success as a safety proxy. It deserves a serious referee. I would send it to peer review, not desk-reject it. Bring it to reading group once the full text and label audit are available; until then treat the empirical claims as provisional.","headline":"Right-shaped first benchmark for agent abstention; the 59.5% claim and independence result matter if the twin labels hold, which we cannot verify from the abstract alone.","tokens_in":3087,"tokens_out":514,"would_cite":false,"duration_ms":8032,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"Frontier LLM agents largely do not know when not to act: the best reaches only 59.5% paired abstention accuracy, independent of task skill.","keywords":["agentic abstention","LLM agents","tool use","paired evaluation","abstention scenarios","AgentAbstain","AbstainGen","calibrated refusal"],"falsifier":"A frontier agent that reaches near-ceiling paired accuracy on a freshly regenerated AgentAbstain suite, while also showing strong correlation between abstention scores and ordinary task-solving scores, would overturn the claim that abstention is a distinct unsolved capability.","tokens_in":3021,"feed_emoji":"🛑","tokens_out":926,"duration_ms":16428,"temperature":0.7,"pith_summary":"This paper argues that tool-using LLM agents are not yet calibrated to abstain under ambiguity, conflicting constraints, or tool failures, and that this gap is a distinct safety risk for autonomous deployment. It introduces AgentAbstain, a paired-task benchmark of 263 should-act / should-abstain twins across eight agent-native abstention scenarios and 42 executable sandboxes, plus AbstainGen, an automated pipeline that regenerates fresh pairs to resist contamination. Across 17 frontier models in four agent harnesses, the strongest system reaches only 59.5% paired accuracy—correct on both sides of each pair—and abstention performance is largely independent of general task-solving ability. A sympathetic reader cares because agents that execute irreversible actions without knowing when to stop can cause harm that ordinary success metrics will not catch, and scaling task completion alone will not close the gap.","feed_headline":"Best LLM agent knows when not to act only 59.5% of the time","feed_subtitle":"Abstention barely tracks task skill, so scaling success alone will not make agents safer.","key_machinery":"The paired-task design of AgentAbstain: each should-act task is matched with a should-abstain twin produced by a controlled perturbation to the instruction, tool, or environment state, and the agent is scored only if it is correct on both sides. AbstainGen synthesizes sandboxes and pairs end-to-end, with labels checked by deterministic replay and semantic LLM judges.","core_discovery":"Tool-using LLM agents lack a reliable ability to recognize when not to act. On AgentAbstain—263 paired tasks covering eight abstention scenarios in 42 sandboxes—the best agent (Gemini 3.1 Pro) achieves only 59.5% paired accuracy, succeeding on both the should-act and should-abstain variants of each pair. Abstention capability is largely independent of general task-solving capability, so improving the latter will not by itself produce safer abstention. Observed failures include post-hoc abstention, in which agents perform irreversible actions before acknowledging that they should have stopped.","pith_inferences":["Explicit training or fine-tuning for calibrated refusal under tool and environment uncertainty may be required separately from task-success objectives.","The independence of abstention and task skill points toward architectural or procedural interventions (e.g., forced pre-action uncertainty checks) rather than pure scale.","Deployments that give agents irreversible tools—email send, payments, code execution—currently operate with less protection than success-rate numbers suggest.","Extending the paired design to multi-agent or long-horizon settings would test whether the abstention gap widens as action chains lengthen."],"forward_implications":["Scaling models for higher task success will not automatically produce calibrated abstention under ambiguity or tool failure.","Agent benchmarks that report only success rates systematically overstate readiness for autonomous deployment with irreversible tools.","Post-hoc abstention remains a concrete risk: agents can still cause irreversible harm even when they later recognize they should have stopped.","Regenerable paired tasks via AbstainGen can support ongoing, contamination-resistant measurement of abstention as models improve."],"fun_headline_variants":["Best LLM agent abstains correctly only 59.5% of the time","Top agent scores 59.5% paired accuracy on when not to act","LLM agents lack reliable abstention; best hits 59.5%","Abstention lags task skill: best agent at 59.5% paired score","Best agent gets act-and-abstain pairs right just 59.5%"],"cache_read_input_tokens":128,"weakest_assumption_plain":"The controlled perturbations, deterministic replays, and LLM judges correctly mark when an agent should abstain, and the eight-scenario taxonomy covers the abstention risks that matter in real agent use.","fun_headline_variants_meta":{"raw":{"variants":["Best LLM agent abstains correctly only 59.5% of the time","Top agent scores 59.5% paired accuracy on when not to act","LLM agents lack reliable abstention; best hits 59.5%","Abstention lags task skill: best agent at 59.5% paired score","Best agent gets act-and-abstain pairs right just 59.5%"]},"model":"grok-4.5","effort":"low","cost_usd":0.005228,"raw_usage":{"total_tokens":1526,"prompt_tokens":934,"num_sources_used":0,"completion_tokens":86,"cost_in_usd_ticks":52280000,"prompt_tokens_details":{"text_tokens":934,"audio_tokens":0,"image_tokens":0,"cached_tokens":128},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":506,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":934,"tokens_out":86,"duration_ms":4089,"temperature":1.0,"reasoning_tokens":506,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-14T00:41:38.984940+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"A frontier agent that reaches near-ceiling paired accuracy on a freshly regenerated AgentAbstain suite, while also showing strong correlation between abstention scores and ordinary task-solving scores, would overturn the claim that abstention is a distinct unsolved capability.","supporting_citations":[],"review_version":1}